In wireless communications systems, different signals often share the same propagation resources. Different communication systems provide various mechanisms for separating signals (e.g., in the time, frequency, or code domain), but separation seldom is perfect because of transmission synchronization errors, Doppler spread or dispersion in the channel, etc. Further, with multiple-input-multiple-output (MIMO) systems, multiple data streams are transmitted to the same user over the same resource space, so that the signal separation relies only on propagation channel differences for the different transmit/receive antenna pairs. Even where MIMO pre-coding is used to improve separation between the data streams, the signals typically are significantly coupled at the receiver.
In all of these cases, the different user signals or the multiple streams interfere with each other at the receiver. The receiver may be understood as receiving a composite signal, including any number of component signals. One or more component signals generally are of interest to the receiver, but the recovery of any given signal of interest is complicated by interference caused by the remaining signals.
A known approach to interference cancellation depends on the detection of interfering signals with the same processing complexity used to detect the desired signal(s). This approach applies full-complexity demodulation and decoding to the interfering signals, including soft value processing. Accurate determination of the interfering signals allows correspondingly accurate reconstruction of the interfering signals for interference cancellation. Full processing of interfering and desired signals, however, imposes a significant signal processing burden on the receiver and makes the approach not feasible or at least undesirable for low-complexity receivers.
Alternatively, full interferer decoding can be avoided by using expected value processing to calculate expected values for interfering symbols. However, calculating the mathematically “exact” expected value for an interfering symbol requires a significant number of computations. For example, for an “L-QAM” constellation, L symbol probabilities Pl need to be computed, each consisting of log2 L products, and the expected symbol value ŝl is produced by performing L multiply-and-accumulate (MAC) operations.